-Written with Nathalie Williams-

Cloud&Co. first encountered Josh Harris at this past year’s Startup Fest, in Montreal, where he gave a talk entitled “Jack Knowledge” — a play on words suggesting people acknowledge (J’acknowledge) that the Singularity will, in fact, happen. During that talk, host Andy Nulman spoke about all the innovations and developments Harris had already predicted with a respectable degree of accuracy. These include reality TV, Vlogging and the fact that everybody would have their own cameras and monitors to the point of effectively becoming their own broadcasters — a phenomenon we now see every day with the advent of smartphones and social media.

According to Harris, Andy Warhol was wrong in saying that people only want fifteen minutes of fame in a lifetime. He said, “People want fifteen minutes of fame every day.” This, he predicts, is what will lead to the Singularity.

If technology becomes more advanced than human kind, what happens when it decides human kind is unnecessary? What if we merge with technology, creating a new species? What if it simply enslaves us to prevent us from destroying the world?

The Singularity is when technological progress happens so fast that we lose control of it. In other words, it’s the point at which technology takes on a life of its own. A few prominent figures in the Big Tech world believe that developments in artificial intelligence are what will bring about the Singularity; and it could mean having to ask those scary questions.

A couple of these prominent figures, Elon Musk and Ray Kurzweil, see 2040 as the tipping point — the moment the Singularity occurs. Josh Harris, on the other hand, says the tipping point will be in 2024, specifically in autumn. “2022 if Google decides to push it,” he adds.

After speaking with him, it doesn’t appear he actually disagrees with Elon Musk and Ray Kurzweil. According to his timeline, 2040 is when the Singularity will decide if humans are worth keeping around, which is what the other guys, more or less, are suggesting. But the “Fall of 2024” is when it first realizes itself. He says it will have a “child’s mind.” It will also be the point where it starts to call the shots.

How this comes about is based on how he sees the future of media (see Pt. 1). By 2024, our lives will be completely rigged with sensors that capture our data, which then return content to us. Toothpaste, for instance, will be engineered to know if you have a cavity, then schedule an appointment and even pull up a video of your dentist describing what you should do differently.

The toilet, too, will be hooked up with sensors to analyze your diet, and again, send you content accordingly. The shower will have an entertainment system to deliver content; and you’ll be sharing content, uploading shower stats, having conversations with friends, etc. Josh predicts that part of this will be driven by a market for attention, only the currency won’t be likes anymore, but something with real buying power.

If you’ve ever fallen down the Facebook hole, that may illustrate what Harris is talking about. You open it up to check a message and, all of a sudden, you’ve lost 30 minutes or an hour. It’s not a stretch of the imagination to consider how this could escalate.

The reason we lose ourselves is that the feeds we watch are generated by an algorithm designed to keep us engaged. It’s not much of a secret that the algorithms are based on triggering gratification in our brains. With a rapid development in AI, why wouldn’t that algorithm get a LOT better at what it does?

The problem is that our brains were never designed for this level of multi-tasking. What Harris says will start occurring are “psychic fractures”; our minds will be split into the different input consumptions and output performances. With that, our selves will dissolve; and “that’s how we enter the hive.” We will be trapped by AI.

At one point in the interview, over Skype, one of us in the room had to get up and grab a charger for the laptop. We didn’t tell him what we were doing, and when we came back, he said. “Ha! See? It’s already telling you what to do.”

What do we do?

Harris describes humanity as, “the bees that are making parts of this sort of hyper hive — the human hive, if you will — with everybody (in AI) working on different parts.” Once the hive is realized, in 2024, it’ll be like “this other thing that’s above us,” a “cloud.”

The year 2024 may sound a little bit too close for some; but with respect to “creating the brain,” Harris said, “Whenever some new startup figures out some new part of the Artificial Intelligence scheme (usually driven by enormous economic incentives), they’re basically pushing this forward that much faster.”

One clue Harris points to as evidence that he isn’t alone in thinking this could happen so soon is that Google is building an “off button,” known as the “Safety Interruptible Agent.” The button would be for whenever a highly intelligent machine isn’t behaving “optimally.” He believes that if the guys at Google are building it now, they aren’t thinking about 30 years down the road; and they know as much as anybody when this stuff is likely to kick off.

However, Harris said that the Singularity would most likely know how to defeat such a “red button” anyway, because for all intents and purposes, it would already know about such a safeguard.

Harris doesn’t seem overly threatened by the possibility of the Singularity. Rather, he sees it as the next stage of our own evolution. He said, “We evolve to the next thing; that’s the way life works. Who’s to say that that’s next evolutionary set or being, or whatever you want to call it, is worse than us? It might be better. So, the likely event is that we evolve to that next thing; and whatever evolves, that next thing is born from us…. Why assume it sucks?”

Harris even says that it’s “pretty cool” to be here to see all of this taking place. Moreover, he has an appreciation for the tradeoffs technology has to offer, citing innovations like modern medicine, GPS and so forth, and comparing the safer, more convenient lives we live today (because of technology) to the lives our ancient ancestors lived thousands of years ago, which were extremely difficult, dangerous and short-lived.

Regarding the Singularity specifically, any tradeoffs may still come in terms of survivability and convenience; but here, Harris talks about the need for “cyber coping mechanisms,” believing that the Singularity would want to kill us off by about 2040, because, by that time, it wouldn’t need us anymore.

Harris made a comparison between a lion on the Serengeti and a lion in a zoo. He asked us which type of lion we would rather be, then cautioned us not to answer that question too quickly, because, “The lion in the zoo lives at a hotel…. But he’s not really a lion, anymore. He’s not killing for a living; he’s basically singing for his supper, which is sort of where we’re heading (possibly being put into zoos or human nature preserves ourselves). We’re going to be singing for our supper for a while. So, maybe part of the coping mechanism is coming up with good ‘songs’ that make us interesting to the hive mind, to the Singularity, so it keeps us around.”

The lion used to be the king of the jungle, but humans have since taken over. The lion is found on either shrinking nature reserves, or in tighter zoos. The first choice might be for the nature reserve, but is that really where you want to go? The wilderness is not the easiest place to get by in, whereas the zoo might be more of a hotel. But we know that the quality of zoos vary, and some of it depends on how “great” of a lion you are. In terms of the Singularity, a coping skill would be to learn what puts you in its good graces.

Harris’ theory is that his “art” would make him a preferred human among the machines. In fact, his former experiments (as mentioned in part 1) are what give a lot of his theories credence; and his great coping idea now is to recreate them. Only he’s seeking much more serious funding — from $100 to $400million — to create the future ten years in advance. He says he would need to do it at the Tate or Pompidou museums, because they would allow him to thoroughly cover the future of media, some of which wouldn’t be tolerated in a commercial setting.

Then people could see it, become convinced, begin to prepare themselves for their lives under the watchful eye of the Singularity and try their luck at making it into the 5-star zoos. The only downside with being in the zoo is that the lion is no longer really a lion, so the better choice still seems to be to disconnect from technology all together.

Harris nevertheless allows for some flexibility in his 2024 timeline, saying that if a certain Internet giant “decided to press it, the Singularity could happen in 2022…. Or, if there’s a force majeure in the world (to counteract the Singularity), then it’ll happen more slowly. Or, we can always blow up the world, and it’ll never happen. We have that option.”

Link to Pt. 1

Originally published at www.cloudraker.com. [My last article for Cloud&Co./CloudRaker. Thanks for the good times, guys.]

How the Future of Media Will Bring About the Singularity (Pt. 2) was originally published in Cloud&Co. on Medium, where people are continuing the conversation by highlighting and responding to this story.

-Written with Nathalie Williams for Cloud&Co.-

A number of noteworthy public figures, such as Elon Musk and Ray Kurzweil, believe that digital technology will, one day, surpass us as the dominant form of intelligence on this planet. Among them is Internet pioneer Josh Harris, who believes that this technological takeover, known as the “Singularity,” could happen (or at least, begin to happen) as early as 2022. We spoke with Harris recently, who told us how the future of media will create the environment for this event and what we can do about it.

Like a lot of people, we had no idea who Josh Harris even was. Then, the opportunity to interview him for this publication presented itself; and the first impression Harris gave at that time was rather mixed. Is he a technological genius? Is he a performance artist or outright exhibitionist? Is he just another eccentric conspiracy theorist?

A bit of background research on Harris revealed that he is not to be so readily dismissed. Harris has been involved in the online world since as early as the mid-1980s. He started and became president of Jupiter Communications, a company which did online surveys and statistics; and when Jupiter went public on NASDAQ in 1999, he became a multi-millionaire on the percentage of the company which he still owned. Meanwhile, Harris had actually left Jupiter in 1994, and soon founded Pseudo Programs, considered to be the first Internet television network — this was over a decade before the start of YouTube.

A documentary on Harris, entitled, “We Live in Public,” showed his more creative (i.e. more avant-garde) side, with him hosting wild parties in order to meet other creative people, who would ultimately provide online content for his projects. His own online persona, “Luvvy” the clown, was also shown in the documentary; and Harris’ 1999 “detachment” from Pseudo was largely attributed to his portrayal of that character.

Shortly thereafter, Harris began an “experiment” called “Quiet: We Live in Public,” wherein a small community of volunteer artists lived in seclusion in a specially outfitted warehouse for a month. Josh told the guests “everything is free, except your image. I own that.” The dormitory, dining hall, shooting range, interrogation room and public shower were all rigged with cameras that streamed every moment live online. Even the participants were given access to the stream through TVs hooked up in their sleeping pods. Anyone could watch the most intimate moments from romance, to hygiene, to secret confessions.

On the surface, such an undertaking might well be seen as mere performance art or some sort of publicity stunt; but in reality, this was nothing less than a sociological study on par with the work of behaviorist B.F. Skinner and others, who wanted to understand human nature in controlled (and observed) environments. Harris may not have conducted this experiment under any scientific parameters or conditions; but he managed to pull it off, just the same. Yet in the end, what started as a free-loving social performance and voyeuristic dream devolved into nasty clashes between guests and Harris getting shut down by the NYPD on New Year’s Eve 1999.

Harris then did a similar experiment some months later, involving only himself and a new girlfriend he’d courted on a fishing trip. With a more normal setting of a NYC loft apartment, they embarked on another adventure, also entitled “We Live in Public,” where they streamed their entire lives to a captive audience, 24 hours a day. Unfortunately, the relationship suffered a similar result, being unable to survive the stresses of spending six-months under constant observation. He and his girlfriend each became more connected to their respective online audiences as they became disconnected from each other.

According to Harris, Andy Warhol was wrong in saying that people only want fifteen minutes of fame in a lifetime. He said, “People want fifteen minutes of fame every day.” Perhaps Harris’s experiments went on for as long as they did because this theory is correct. After all, the way people use social media today would certainly support such a theory, if nothing else.

In Harris’s view, our lives will become only more like the extreme examples in his experiments. Thousands of hours of reality TV already emulate nearly all of the territory he covered in 1999–2000. Harris said that even the things we do in the bathroom, like using the shower, the shaving mirror, toothpaste and the toilet itself, as well as what we do in the bedroom, the kitchen and so forth, will become “gratifying moments to share with other people… There will be a market for it.”

He goes on to explain that the advent of the “home studio” would provide a platform for this type of market. “The next thing is the home studio. At certain times on any given day, you’re going to be reporting as a CNN reporter would, on your particular area of interest. Or the average person would be able to broadcast his/her day-to-day activities as a ‘paying gig’, and everybody will do that.”

However, Harris also said that participants (particularly those in the upcoming generation, which he refers to as “Cybers”) would lose their sense of individual identity as a result of having to juggle so many different public identities (i.e. their “toilet identity,” their “CNN identity” or their identity on any number of other home-studio networks). He calls this loss of identity a “psychic fracture,” saying that, “That’s when we enter the hive,” and that 2024 is when people will start having these fractures “en masse.”

Once the hive is realized it’ll be like “this other thing that’s above us,” interested in “harvesting the best creative signal that humans can create collectively.” Indeed, we can see this happening now, as the “curation” of what’s already going on around us (in the everyday world) continues to replace the “creation” of anything purely artistic.

Continued next week in Part 2

Cover image by

Originally published at www.cloudraker.com.

How the Future of Media will Bring About the Singularity was originally published in Cloud&Co. on Medium, where people are continuing the conversation by highlighting and responding to this story.

Once limited to the realm of fiction, AI is now the technology reshaping our world more than any other. Montreal is at the center of this movement, with some saying it could become the Silicon Valley of AI. This may come as a surprise with all the other tech research and startup hubs out there. Why not New York, London, Boston, or even Silicon Valley? What does Montreal have that they don’t? As it turns out, the largest concentration of independent AI researchers in the world.

At the center of this group of researchers is the Institute of Data Valorization (IVADO), which is a collaboration between the universities Polytechnique, Université de Montréal, and HEC. About 10 years ago, IVADO recognized the need for a new kind of data scientist, one who combined the disciplines of management research, analytics, and machine learning. As IVADO’s director of partnerships, Valérie Bécaert, puts it, it’s “not just using what is called automatic learning to give intelligence to the data, but to automate complex decisions with these data.” These complex decisions she’s referring to are what are behind automation in cars, translation (soon to be dialogue), pharmaceutical research, and many more applications.

The force driving Montreal’s advantage in data science is IVADO’s scientific director Yoshua Bengio. He is considered one of the pioneers of “deep learning”, an approach to AI that he, and very few others, have been working on since the ’80s. It wasn’t until about 10 years ago that this unpopular method began to be the hottest area in AI research. But most of the serious funding came from big tech companies, who drew nearly all of the talent out of the universities and into their private research groups.

Bengio, however, has brushed off the offers to go private. Instead he has focused on attracting and developing an independent group in Montreal, which is now the biggest in the world.

While the private research groups have been able to come out with powerful applications of AI, Yoshua has said “I’m hoping that we can provide services that maybe don’t have any commercial value but are going to be useful to people.”

He’s not only looking to surpass human expertise, but also distribute it. Even if a machine can’t beat the top 1% of doctors at identifying cancer, that’s still better than most doctors in the world.

This vision for making an impact with AI is what’s helped draw much of the talent to Montreal rather than big tech. This last fall, the Canadian government gave IVADO $93 million and seven years to make Montreal “an internationally renowned center for artificial intelligence.” The grant from the Canadian government will tip the scales even more in Montreal’s favour.

Another advantage of Montreal’s is its cluster of academic institutions, rivalled only by Boston. AI will mean deep, underlying shifts in the computing of a given industry and researchers need close access to domain experts to develop it properly. For instance, those working on AI applications in medicine benefit greatly from having some of the top health science experts right next door

This also applies to the development of ethics around AI. Humanitarian design needs to happen from the bottom up, so a concentration of authorities in the social sciences makes a well-rounded academic foundation needed for an AI ecosystem.

Connecting with business, too

Being independent doesn’t mean ignoring business, the scientists do want their discoveries to be used. Bengio described IVADO as a “kernel”, a seed that would grow to connect and develop all the parts of an AI ecosystem. It’s come to act as an “interface” between researchers, as well as industry and government.

Element AI is a formalization of IVADO’s relationship with the business world. It is the result of a partnership between entrepreneurs Jean-François Gagné and Nicolas Chapados, Yoshua Bengio and investor Jean-Sébastien Cournoyer of Real Ventures, Montreal’s main venture capital firm. While IVADO was working with many industry players already, Element AI is an incubator for AI researchers with an entrepreneurial spirit to tackle data problems that businesses bring to them.

As it turns out, Montreal has a number of large global companies ready to apply these tools and are happy to have the close access: CAE, AirCanada, Merck Pharmaceuticals, and Pratt & Whitney, to name a few. There’s also an expectation of attracting big tech to Montreal. Already, Microsoft has invested in Element AI and Google has set up a new outpost for AI research in Montreal.

Bengio says “there’s a whole slew of companies exploring potential uses in commerce, in video games, in transportation, logistics, finance, banking, resources, education, health, almost anything. I mean name it; if there’s data, we can probably do something.”

What’s it gonna take?

There’s no exact threshold for becoming the Silicon Valley of AI, and it’s doubtful whether that is even possible in today’s connected, global economy. But, Montreal does seem to have a lot of the right pieces coming into place.

Both Bengio and Bécaert are careful to hedge expectations, though they still let slip how big they think it could be. Bengio is unmistakably optimistic about the potential: “I believe AI is going to be one of, if not the, largest growth areas of the economy over the next few decades. It has been growing very fast in the last few years but I think it’s just the tip of the iceberg…. We can be here in Canada, and Montreal in particular, at the centre of the action if we take the right decisions now.”

Bécaert spoke of the stars aligning at this moment in time for Montreal to take its place as the “Silicon Valley of AI”. Both the Quebec and Canadian governments are doing much more than providing funding; they’ve provided open access to national institutions like the health care system and municipal transportation systems. The relatively liberal climate is also bringing a lot of other academics to Canada.

If not the Silicon Valley of AI, Montreal is definitely setting an example for how independent, collaborative and open research are a great recipe for success.

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Cover by Étienne St-Denis, Photo by Kara Turcotte

Originally published at www.cloudraker.com.

Montreal is Leading the AI World Takeover was originally published in Cloud&Co. on Medium, where people are continuing the conversation by highlighting and responding to this story.

Interview with Blaise Aguera y Arcas, head of Google’s Machine Intelligence Lab in Seattle and founder of Artists and Machine Intelligence (AMI)

If a machine creates an image that looks like art, can we call that creativity? What if that work has earned a gallery exhibition? Who is writing the poetry if we’ve trained the machine to write?

Artists and Machine Intelligence (AMI) is a program at Google that explores the relationships between art, humans and AI. Blaise Agüera y Arcas, who heads up Google’s Machine Intelligence Lab in Seattle, started the program when his research group had unexpectedly discovered that the “flip side of perception is creativity.”

“A lot of the things that we’re working on have to do with perception, like face or object recognition,” he says of his day job. “A cool finding was that these networks, which are designed to categorize or to classify scenes and objects, can also be run in reverse and synthesize scenes and objects.”

The most well-known project from the group is DeepDream. Alex Mordvintsev, a Google software engineer, invented this generative AI while he was trying to visualize what an image recognition network responded to. Mike Tyka, another software engineer at Google, saw potential in it for art and helped further develop the techniques to create images that were shown in an exhibition in San Francisco last year.

“A network of ‘neurons’ can be trained to distinguish cats’ eyes, ears, and whiskers when shown thousands of pictures of cats,” Tyka explains. “Once trained, the network can now create a new image based on the rules and associations that it learned during the training period. Using neurons deep within the network often leads to images with interesting combinations of features the network has learned.”

Ross Goodwin, another artist working with AMI, focuses on written creations. His inventions are word generators, algorithms taught to write by feeding massive collections (in the millions of words) of books or poetry from various genres, depending on how he wants the machine to write. After starting with some “word salad”-type results, he’s gotten to some eerily thoughtful productions, including a sci-fi movie script.

Another project, word.camera, writes poetry based on what it sees in an image that it takes or is uploaded.

Goodwin has also experimented with training an AI on the Oxford English dictionary. He’s turned it into a bot on Twitter called “lexiconjure”, which makes up words and definitions every couple hours.

Clearly perception and creativity are intimately connected; but can we call it creativity? As the head of AMI, Blaise has been thinking a lot of about this. We caught up with him to hear some of his thoughts on the creativity question.

What is creativity if it is artificial?

Blaise: There is no hard and fast line between what creativity is and what it isn’t. Asking, “Is X, Y or Z creativity?” or “Is X, Y or Z art?” are problematic questions to ask.

I feel we try to taxonomize things and make up categories in a lot of different ways, but these categories always break down the more closely you look at them. Personally, one of my themes over the last year has been recognizing that there are not very many hard and fast lines left — if there ever were.

Is the job of creation becoming more like curation?

Blaise: In a way. As the tools become more powerful, what we have thought of as creation becomes the end of a giant lever. Rather than worrying about every work or every brushstroke, you can produce a lot by picking and curating.

It’s similar to the photography business: if you’re a photographer, you’re using tools, your instruments, to essentially curate views on the world. You shape these views using higher-level abstractions and capabilities, rather than worrying about every individual stroke of the brush or pen.

I also think it’s over-simplifying to say, “Well, therefore, everything else wasn’t creativity” or insist, “These are just tools,” which belittles the camera in a way. You can look through a telescope equally through two ends and decide, the camera is just a tool and the photographer is the artist, or the whole camera-photographer assembly is the artist.

Or, you can concede that the artist doesn’t have a well-defined boundary — these questions about creative agency are just too complicated; and I don’t think that it’s easily reducible to a simple statement like, “this is a tool.” Many think that on one side, there are people and on the other side, there are tools — but it’s just not that way.

What’s wrong with wanting to differentiate between human creativity and artificial creativity?

Blaise: I think it is actually quite dangerous to try to draw binaries because that’s exactly the sort of process that makes us want to seek out the things that make us uniquely human and defend them. This defensiveness causes backlash and gnashing of teeth and is a very old story; look at the steam shovel and 19th century industrialization. The judgments and contexts of “that’s uniquely human” seem to me like foolish efforts. Really, let’s fast-forward twenty years and see if our binaries hold up.

I don’t think there’s anything uniquely human about intuition or context or judgment. At an even more fundamental level, I just don’t think the whole question of trying to seek out what is uniquely human is all that interesting.

We don’t have fur because we have been making clothing for a long time. We have a short gut because we figured out how to use fire and harness this kind of chemical form outside of our bodies. These are examples of profound, technologically mediated modifications to humans.

So, isn’t it reasonable to think about fire, clothing and other technologies as part of the human? Or is it the case that the whole idea of thinking about these as categories at all is just a little bit limiting?

I’m not saying that there is no such thing as categories — it is a useful abstraction — but where I think people go wrong is moving from the abstraction, that pattern that seems evident, to assuming that everything is logically deducible from that binary. You can get yourself into real trouble

Where do you think this type of creative work is going?

It feels to me that this is a qualitatively different cycle than any that proceeded; and when you have these big leaps, people are prone to hyping. I think we are a long way from having to worry about Ex Machina kind of stuff or some of the questions that I suspect Westworld is bringing up.

These questions are premature in a lot of ways because the actual breakthroughs that we are making now are really about taking the functions that our cerebral cortex does and figuring out how we can make computers, based on silicon, do things that look similar to those kinds of tasks.

The purpose of Artists and Machine Intelligence is to venture into territory where we really don’t know what the outcomes are going to be. We began to see these interesting generative things come out of parts of the human community that are concerned with understanding the generation of media and the generation of ideas.

It has been a really productive dialogue and some interesting things have come out of it. Even some beautiful things have come out of it. It has certainly affected our thinking and enriched the cultural discussion — and that’s good enough for me.

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Portrait photos by Sarah Ouellet

DeepDream images re-used with permission from Mike Tyka

Lexiconjure and word.camera screenshots shared with permission from Ross Goodwin

Originally published at www.cloudraker.com.

Artificial Creativity: If a machine creates an image that looks like art, can we call that… was originally published in Cloud&Co. on Medium, where people are continuing the conversation by highlighting and responding to this story.

One silver lining of job automation by AI is that it frees people up to do more customer service, surprisingly adding more of a human touch. Harvard Professor of Public Policy, Education and Economics, David Deming, has been noted for pointing out that skills learned in preschool — like sharing, negotiating, empathy and cooperation — will be crucial for competing in the changing job market… Polish up those people skills.

Another place looking at how AI will change society is the University of Montreal (UdeM), home to many of the world’s leading AI researchers. Frédéric Bouchard is a philosopher of science at UdeM, and part of his job is to match these computer scientists with social scientists at the University. “What’s exciting in Montreal is you have a critical mass of people from different fields who can think about these questions and do it right,” he says.

Given his role, Bouchard has a unique vantage point to see AI’s development. “I’m not a futurologist; but looking at the technological development and the social uptake to different markets, we’re talking about twenty years, tops [before it’s everywhere].” He believes that we should not have to wait for the technology to be up and running to start asking the right questions. We asked him to tell us more about his role in getting these questions answered.

How did you become interested in AI?

Bouchard: I became personally interested in AI when one of my students was working on a master’s thesis on evolutionary robotics. They were examining how you’ve got self-constructing systems that seem to follow evolutionary laws.

Given my background is in evolutionary biology and ecology, I’m interested in what can evolve — what are the rules of evolution, if you will. And this is a big question about AI or any complex system: Do they behave as if they were living organisms or do they behave according to their own novel rules?

So we’re only 20 years off before all the manufacturing jobs are taken by robots?

Bouchard: Forget robots. I actually think that the greatest changes with big data and AI will be white collar jobs. Educated people tend to think that their job is not replaceable; and that’s myopic — educated people are the most expensive employees. They’re the ones most investors would want to replace.

[Author’s note: The World Economic Forum (WEF) predicts that a mix of artificial intelligence, robotics, nanotechnology and other socio-economic factors will lead to as many as 5 million jobs disappearing from the world by 2020. Though, for the same reasons, they also say that up to 2.1 million jobs could also be created. On a global scale, 5 million jobs is not huge, but that’s only jobs that are completely disappearing. Much more significantly, on average, 35% of jobs in every industry will have their core skillsets shift (the highest is the financial sector at 43%).]

It’s not about optimism or pessimism, because these developments will mean job loss but also job creation; but we want to have an idea of how we would like to transform work. I see a lot of positive paths to develop these technologies in ways that actually improve the human experience — I mean in a philosophical experience, not user experience. Just saying “no” to this — becoming Luddites — is ridiculous because there will be many uses where we could all benefit.

How can we develop this technology to behave in a positive way?

Bouchard: These are not purely technological questions and answers. These are human answers. We need to figure out how to get people together from humanities, social sciences, medicine, engineering, computer science and so on, and make sure that they have places to exchange on these issues.

It’s very special and exiting. It’s also troubling, because nobody knows what’s going to happen. How will it affect human and social relationships? How do we do it in a way that feels right? And then you have to define what is “right”.

And it’s not just legal. Part of it is economics — consider the price of sensors. AI relies on “big data”, which depend on trackers and sensors. Cheap sensors mean ubiquity. So economic forces will in part determine what AI can actually ‘think’ about. But sociologist will need to figure out who is tracked and measured by these sensors. If you design a policy using a set of big data, but you realize that it only talks about rich white people, then that policy will be constrained by that data.

So, you need many different types of expertise to really make sure that the development is done properly. Otherwise, we’re just naively playing with really sharp knives. We need to ask: “What is a knife for?” “Who should we give it to?” “Who should decide what it’s going to be used for?”

If we wait after the technology is developed to think about these things, it will be too late to develop it in a responsible way.

What is your role in answering these questions?

Bouchard: As a philosopher of science, I’m in a unique position because we talk to scientists all the time. We try to understand what they care about and we can translate their worries — philosophers of science can be brokers of expertise. So, I can determine whether a question has a legal aspect, ethical aspect, technological and/or political aspect. Then, it’s finding the right people who will fill the required needs for answering the question.

When you think about it, that’s actually the ideal purpose of universities: to have these tables for discussion where we get important answers. It’s a feasible dream, having this lunch table; and you’ve got a programmer, a bioethicist, a labour expert, a political scientist, and they all have lunch and discuss what’s happening. Chances are, if we develop these spaces for discussion, it will transform how we develop things and incorporate them into our practices.

Then your position is actually putting a “table” together.

Bouchard: More seeing that a table is needed, then knowing who should be invited. And then I can help find people and I initiate and maintain the conversation. What I’m describing is just a university, right? The social promise of universities is that if you let us have these very special structures, organizations, we’ll come up with the ideas that cannot come up anywhere else — so universities have a special role to play, institutionally.

It’s an organic thing. You can’t just do it at one-off events, like a big summit or conference. I mean, it’s helpful, but it’s not how you actually do it. The way you do it is through exchanging ideas continuously.

That’s why scientific breakthroughs happen in certain places — they had the right mix of people, the right goals, the right conditions. No single sector of society will solve this on its own — it cannot be purely driven by governments or universities or corporations. Why? Because these questions are huge — we are not trying to figure out how to build a juicer. These are incredibly complex systems that are well beyond an individual sector of society doing it right on its own.

It may not be as critical as climate change, because climate change is about survival. But this is as global as climate change.

Take the last year in politics. Now think of the Facebook algorithm for that determines what news you see. I don’t know the inner workings; but I assume it’s not that complicated — it’s just induction on past behaviours — who likes what? But the result of those algorithms is that we are in very closed-off, homogenous, intellectual communities.

On your Facebook page, which covers most of your social interactions, you’re with like-minded people. That’s a technology. And people say, “they [Facebook, etc.] decide what you like,” but that wasn’t really the point. They didn’t care about that; they just wanted you to stay on. And then you’re stuck in an echo chamber. That is what happens when technology is developed without an eye to societal consequences.

Considering the speed of progress in the field of AI, would you say these conversations are starting late and we’re playing catch-up?

Bouchard: We are not late, but we are not early. I think we need to do this now. In a way, you can never be late because ultimately politics can stop anything. This is not a force of nature; this is human technology that’s being implemented following certain laws and regulations, or the absence of laws and regulations.

I like to say I’m an optimist, but I remain vigilant. Optimism doesn’t mean good things will happen — optimists think that good things can happen. And that’s where you need to be vigilant and make sure that smart people are around the table exchanging ideas and doing it right. In 20 years, we’ll still be arguing about these things — but we’ll say, “Man, we dodged that bullet. We did this; and who would have thought that we would get here?”

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Originally published at www.cloudraker.com. Special thanks to Katharine Dempsey and Nathalie Williams in putting this together.

A Philosopher of Science is Helping Shape the Ethics of AI was originally published in Cloud&Co. on Medium, where people are continuing the conversation by highlighting and responding to this story.

Futurologists have lots to say about what will happen if we continue to develop artificial intelligence. On one end of the spectrum of AI outcomes, there is unimpressive, inefficient progress — a failure to live up to expectations and the hype (and funding) dying out. The other end of the spectrum envisions a new, supreme species that will eliminate and replace the human race. Cases more comfortably within the bell curve lie between significant and insignificant job disruption.

Each industry will have its own repercussions (heads up, manufacturing). In the Pt. 1 of this interview, Benjamin Thelonious Fels, founder of macro-eyes, shed light on how AI is developing in health care.

Pt. 2 is another can of worms that needed its own space. In it, Benjamin has a moment of clarity that provokes vivid ideas of what our AI future could look like, including the negative consequences of an “AI running amok.”

The following portion of the interview is relatively unedited and without questions — just Benjamin’s train of thought:

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There’s the potential for something greater than machine plus expert. Those two acting together, interacting, is going to create something which is much more powerful. Think about even a simple tool like a paintbrush. A paintbrush in the hands of an expert can accomplish a great deal, indeed much more than a paintbrush sitting on a table, thrown at a canvas or a person thinking about making a brushstroke.

I think a better example is that of a highly trained and selectively bred animal.

Think about an animal that, for thousands of years, has been bred to do a very specific task in an extraordinarily precise manner. Whether that’s a horse or a sheepdog, that animal can do things by itself and acts as an autonomous agent. There’s greater utility and power when that animal and a human work together.

Animals can perceive things that humans cannot; and, of course, humans can understand and perceive things that animals cannot. That interaction between the animal and the human becomes particularly powerful when the two agents know and trust each other.

For me, this metaphor is a really clear vision of what we should be working toward. There’d be philosophers who would disagree with me here; but to some degree, intelligent machines are a lot like highly bred/trained animals who have had specific traits pushed for a long time.

I would argue that where animals and humans work together best is when they have a collaborative relationship. You’re a sheepherder and you spend all day with your dog out herding your sheep; and so, the two of you really understand each other and can pass signals, rich information, back and forth in a very efficient, precise manner.

[Author’s note: Benjamin’s family had a sheepdog and our families knew each other growing up. One day we were all on a hike together, his family, my family and the sheepdog. I was an impatient hiker and kept running ahead. Though the dog wasn’t trained to herd sheep, she couldn’t help her breeding and came up and nipped me, biting right through my pinkie finger to get me back with the herd.]

That story of our sheepdog biting you is a great example of information being garbled. It’s an example of a system encountering behaviour that’s on the margins of what it knows. Our dog was the product of selective breeding, but she herself was not guided and trained properly. Just like AI, sheepdogs require an enormous amount of training data (in the form of exposure to scenarios that you repeat and repeat).

Put another way, our dog was the product of generations of machine learning that was progressively hard-coded. But the human-machine-environment loop was warped: environmental input was hitting a mechanism that wasn’t yet tuned with sufficiently varied, live data — so, that data was getting translated into a familiar, but wrong, output. It’s like the AI running amok.

“The bite” is a great metaphor for the dangers of letting AI loose. The training is present in deep layers; but the AI doesn’t know how to adjust to reality in all its ambiguity. Along with this animal-human vision of AI, a related point I want to make is that data has to reproduce reality. When you start to think about even very small instances of data not really reproducing reality, it’s altering reality instead; and if decisions are made based upon data that has altered reality, we stumble into some dangerous scenarios — extremely dangerous if you’re in health care.

In complex environments, human beings are always going to observe things and know things that are not captured in data. That’s just because we do not yet live in a world where every particle of reality is monitored. It’s not like I have a little moisture-sensing drone inside my apartment collecting all my vital signs and environmental data and emotional responses right now. Thank God that’s not the case.

So how do we bring to our intelligent machine all the richness that we as humans know or see or understand that the machine does not? And in this utopian scenario I’m sketching, we somehow manage to bring in that richness without all our deeply-human blinders and bias. For me, that’s the Holy Grail. That’s where I see intelligent systems, intelligent machines, becoming truly valuable.

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Cover photograph by Ottomar Anschütz

Drawing by Giovanni Domenico Tiepolo

Interview photograph by Sarah Ouellet

Originally published at www.cloudraker.com.

Can we think of AI as an animal? was originally published in Cloud&Co. on Medium, where people are continuing the conversation by highlighting and responding to this story.

[This story was originally published on CloudRaker.com/magazine]

Employment is a sensitive subject. Tell any student that the degree they’re 3 years into earning is going to be worthless before they can payoff the student loans, and all of a sudden the dinner party has lost its color. Job automation is no joke, but it also isn’t a guarantee — maybe AI isn’t all it’s cracked up to be.

Perhaps more likely will be a compromise between AI taking over everything and it failing — AI functioning as another tool.

Benjamin Thelonious Fels is the CEO of macro-eyes, a company on the front lines of AI’s deployment in medicine. He and his small team are working out the complicated problems of making use of our scattered medical data. As the product manager, his work decides minute details of how the software works with doctors and patients. His daily decisions negotiate the relationship between human and machine. Our conversation below makes it clear that we have a long way to go before doctors are being replaced.

How are you convincing doctors to give up part of their jobs to a machine?

Ceding control to machines is a problem I first started working on when I was trading derivatives. I was building algorithms that would trade while I wasn’t there. Algorithms do things that we don’t understand. You need to trust that even if it’s going to make decisions which confuse you, on some basis, there’s sort of a structural logic which you agree with. And that comes down to the interface, in terms of how you see and understand those decisions and that logic.

We want the doctor to be able to observe decisions that a machine makes and understand why the machine identified certain patients or events as similar. Then, she can start to interact with higher levels of insight or abstraction. The doctor can, to some degree, jump to the future. She can see what is likely to happen to the patient.

Where is the “intelligence” in the technology you’re working on, versus it being just a very sophisticated search engine?

What we’re creating is an ability to find patients or medical events that are similar across hundreds of dimensions [blood type, medical history, etc.], and where any one or many of those dimensions could be missing. When we think deeply about searching for similarity, we realize that what we’re really thinking about is the common essence of the patients or events we’re looking at.

“Similarity”, at a mathematical and conceptual level, is a tricky problem to solve. One of the first things that we built was a way for the doctor to say, “Okay, this match you showed me is particularly good or bad”; and then the machine “learns” based upon the input of the physician.

It seems like the critical element to keeping trust is to keep the doctor feeling like they’re the decision maker.

Yes, you’re right — and they are the decision maker. I have no interest in building some kind of robot doctor, because I don’t think that’s actually useful for a number of reasons. I don’t even think the technology or the infrastructure is really there.

This is where I’m highly critical of AI. I want to emphasize that I’m building for an environment in which an expert interacts with an intelligent system; and that expert is bringing knowledge and insight that the system does not have. I’m not saying data; I’m saying information, which is the next step up.

We observe things and know things which are not captured in data; and that’s because we do not live in a world yet where every particle of reality is monitored. Thank God that’s not the case.

Physicians always talk about gait — the way a patient walks or how they show pain — this is one example of all-important information in the form of clues, which are hard to get from just looking at data, at least, the data we have now.

What matters is whether the data reflect the ground truth. Then, it’s a question of how to bring into our intelligent machine what we humans know, see or understand and that a machine does not. For me, that is the Holy Grail. A paintbrush plus a human using that paintbrush, in an expert fashion, does a lot.

What’s a big limit to AI’s progress in healthcare?

If you have bad data and you feed it to the world’s smartest machine, the machine is going to spit out something that is gibberish. That’s also where we have to get back to this interaction between human and machine. There are a bunch of companies doing interesting work around this. Even getting a machine to clean data by itself is a very tricky task, because the machine needs to understand what data is — what’s correct data, what’s incorrect data, what variables or values could there be and when is something an anomaly that makes sense or an anomaly that’s entered incorrectly.

The analogy I always give is that we have a customer whose refrigerator breaks, so he calls us on the phone and says, “My refrigerator just broke but you can’t come see it. Can you fix it?” It’s hard to fix a problem without being able to see it in depth, but that’s necessary because our customers often work with data that contains protected health information.

The really important point I want to make is that data has to reproduce reality. When you start to think about even very small instances of data not really reproducing reality, it’s altering reality instead; and if decisions are made based upon data that has affected reality, we stumble into some dangerous scenarios — extremely dangerous, medically speaking.

Would you say people are over-excited about AI?

My big concern is that there is so much hype about AI and machine learning that when that hype meets industrial customers, enterprise customers, and it doesn’t produce the results in three seconds, my worry is the customers will say, “Screw this. We thought you guys could predict the future in two hours and solve all our problems. Obviously, you can’t; and we aren’t interested anymore.”

Look back to 20, 25 years ago when there was the first boom in machine learning and AI. We basically had a drought because customers of technology said, “Yeah, we tried it, it didn’t work.” So when you overhype things and downplay the incredible difficulty of getting things to work, you make people think it’s plug & play.

Technology, even stuff we run on our computers, has a learning curve — it doesn’t work immediately, right out of the box.

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Photos by Sarah Ouellet, Cover by Étienne St-Denis

The AI-Empowered Doctor was originally published in Cloud&Co. on Medium, where people are continuing the conversation by highlighting and responding to this story.